Chapter 19
Algorithms for
Query Processing
and Optimization
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley
0. Introduction to Query Processing (1)

Query optimization:


The process of choosing a suitable execution
strategy for processing a query.
Two internal representations of a query:


Query Tree
Query Graph
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Introduction to Query Processing (2)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
1. Translating SQL Queries into Relational
Algebra (1)

Query block:




The basic unit that can be translated into the
algebraic operators and optimized.
A query block contains a single SELECT-FROMWHERE expression, as well as GROUP BY and
HAVING clause if these are part of the block.
Nested queries within a query are identified as
separate query blocks.
Aggregate operators in SQL must be included in
the extended algebra.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Translating SQL Queries into Relational
Algebra (2)
SELECT
FROM
WHERE
SELECT
FROM
WHERE
LNAME, FNAME
EMPLOYEE
SALARY > (
SELECT
FROM
WHERE
LNAME, FNAME
EMPLOYEE
SALARY > C
πLNAME, FNAME (σSALARY>C(EMPLOYEE))
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
SELECT
FROM
WHERE
MAX (SALARY)
EMPLOYEE
DNO = 5);
MAX (SALARY)
EMPLOYEE
DNO = 5
ℱMAX SALARY (σDNO=5 (EMPLOYEE))
2. Algorithms for External Sorting (1)

External sorting:


Refers to sorting algorithms that are suitable for large files
of records stored on disk that do not fit entirely in main
memory, such as most database files.
Sort-Merge strategy:






Starts by sorting small subfiles (runs) of the main file and
then merges the sorted runs, creating larger sorted subfiles
that are merged in turn.
Sorting phase: nR = (b/nB)
Merging phase: dM = Min (nB-1, nR); nP = (logdM(nR))
nR: number of initial runs; b: number of file blocks;
nB: available buffer space; dM: degree of merging;
nP: number of passes.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms
for External
Sorting (2)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
3. Algorithms for SELECT and JOIN
Operations (1)

Implementing the SELECT Operation

Examples:





(OP1): s SSN='123456789' (EMPLOYEE)
(OP2): s DNUMBER>5(DEPARTMENT)
(OP3): s DNO=5(EMPLOYEE)
(OP4): s DNO=5 AND SALARY>30000 AND SEX=F(EMPLOYEE)
(OP5): s ESSN=123456789 AND PNO=10(WORKS_ON)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (2)


Implementing the SELECT Operation (contd.):
Search Methods for Simple Selection:

S1 Linear search (brute force):


S2 Binary search:


Retrieve every record in the file, and test whether its attribute
values satisfy the selection condition.
If the selection condition involves an equality comparison on a
key attribute on which the file is ordered, binary search (which
is more efficient than linear search) can be used. (See OP1).
S3 Using a primary index or hash key to retrieve a
single record:

If the selection condition involves an equality comparison on a
key attribute with a primary index (or a hash key), use the
primary index (or the hash key) to retrieve the record.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (3)


Implementing the SELECT Operation (contd.):
Search Methods for Simple Selection:
 S4 Using a primary index to retrieve multiple records:


S5 Using a clustering index to retrieve multiple records:


If the comparison condition is >, ≥, <, or ≤ on a key field with a
primary index, use the index to find the record satisfying the
corresponding equality condition, then retrieve all subsequent records
in the (ordered) file.
If the selection condition involves an equality comparison on a nonkey attribute with a clustering index, use the clustering index to
retrieve all the records satisfying the selection condition.
S6 Using a secondary (B+-tree) index:


On an equality comparison, this search method can be used to
retrieve a single record if the indexing field has unique values (is a
key) or to retrieve multiple records if the indexing field is not a key.
In addition, it can be used to retrieve records on conditions involving
>,>=, <, or <=. (FOR RANGE QUERIES)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (4)


Implementing the SELECT Operation (contd.):
Search Methods for Simple Selection:

S7 Conjunctive selection:
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If an attribute involved in any single simple condition in the
conjunctive condition has an access path that permits the use
of one of the methods S2 to S6, use that condition to retrieve
the records and then check whether each retrieved record
satisfies the remaining simple conditions in the conjunctive
condition.
S8 Conjunctive selection using a composite index

If two or more attributes are involved in equality conditions in
the conjunctive condition and a composite index (or hash
structure) exists on the combined field, we can use the index
directly.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (5)


Implementing the SELECT Operation (contd.):
Search Methods for Complex Selection:

S9 Conjunctive selection by intersection of record
pointers:




This method is possible if secondary indexes are available on
all (or some of) the fields involved in equality comparison
conditions in the conjunctive condition and if the indexes
include record pointers (rather than block pointers).
Each index can be used to retrieve the record pointers that
satisfy the individual condition.
The intersection of these sets of record pointers gives the
record pointers that satisfy the conjunctive condition, which are
then used to retrieve those records directly.
If only some of the conditions have secondary indexes, each
retrieved record is further tested to determine whether it
satisfies the remaining conditions.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (7)

Implementing the SELECT Operation (contd.):

Whenever a single condition specifies the selection, we
can only check whether an access path exists on the
attribute involved in that condition.



If an access path exists, the method corresponding to that
access path is used; otherwise, the “brute force” linear search
approach of method S1 is used. (See OP1, OP2 and OP3)
For conjunctive selection conditions, whenever more
than one of the attributes involved in the conditions have an
access path, query optimization should be done to choose
the access path that retrieves the fewest records in the
most efficient way.
Disjunctive selection conditions
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (8)

Implementing the JOIN Operation:

Join (EQUIJOIN, NATURAL JOIN)
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
two–way join: a join on two files
e.g. R A=B S
multi-way joins: joins involving more than two files.
e.g. R A=B S C=D T
Examples


(OP6): EMPLOYEE DNO=DNUMBER DEPARTMENT
(OP7): DEPARTMENT MGRSSN=SSN EMPLOYEE
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (9)


Implementing the JOIN Operation (contd.):
Methods for implementing joins:

J1 Nested-loop join (brute force):


For each record t in R (outer loop), retrieve every record s from
S (inner loop) and test whether the two records satisfy the join
condition t[A] = s[B].
J2 Single-loop join (Using an access structure to retrieve
the matching records):

If an index (or hash key) exists for one of the two join attributes
— say, B of S — retrieve each record t in R, one at a time, and
then use the access structure to retrieve directly all matching
records s from S that satisfy s[B] = t[A].
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (10)


Implementing the JOIN Operation (contd.):
Methods for implementing joins:

J3 Sort-merge join:



If the records of R and S are physically sorted (ordered) by
value of the join attributes A and B, respectively, we can
implement the join in the most efficient way possible.
Both files are scanned in order of the join attributes, matching
the records that have the same values for A and B.
In this method, the records of each file are scanned only once
each for matching with the other file—unless both A and B are
non-key attributes, in which case the method needs to be
modified slightly.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Algorithms for SELECT and JOIN
Operations (11)


Implementing the JOIN Operation (contd.):
Methods for implementing joins:

J4 Hash-join:



The records of files R and S are both hashed to the
same hash file, using the same hashing function on
the join attributes A of R and B of S as hash keys.
A single pass through the file with fewer records
(say, R) hashes its records to the hash file buckets.
A single pass through the other file (S) then hashes
each of its records to the appropriate bucket, where
the record is combined with all matching records
from R.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
6. Combining Operations using Pipelining (1)

Motivation




A query is mapped into a sequence of operations.
Each execution of an operation produces a temporary
result.
Generating and saving temporary files on disk is time
consuming and expensive.
Alternative:


Avoid constructing temporary results as much as possible.
Pipeline the data through multiple operations - pass the
result of a previous operator to the next without waiting to
complete the previous operation.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Combining Operations using Pipelining (2)

Example:




For a 2-way join, combine the 2 selections on the
input and one projection on the output with the
Join.
Dynamic generation of code to allow for multiple
operations to be pipelined.
Results of a select operation are fed in a
"Pipeline" to the join algorithm.
Also known as stream-based processing.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
7. Using Heuristics in Query Optimization (1)

Process for heuristics optimization
1. The parser of a high-level query generates an initial
internal representation;
2. Apply heuristics rules to optimize the internal
representation.
3. A query execution plan is generated to execute groups of
operations based on the access paths available on the files
involved in the query.

The main heuristic is to apply first the operations that
reduce the size of intermediate results.

E.g., Apply SELECT and PROJECT operations before
applying the JOIN or other binary operations.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (2)

Query tree:



A tree data structure that corresponds to a relational
algebra expression. It represents the input relations of the
query as leaf nodes of the tree, and represents the
relational algebra operations as internal nodes.
An execution of the query tree consists of executing an
internal node operation whenever its operands are
available and then replacing that internal node by the
relation that results from executing the operation.
Query graph:

A graph data structure that corresponds to a relational
calculus expression. It does not indicate an order on which
operations to perform first. There is only a single graph
corresponding to each query.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (3)


Example:
 For every project located in ‘Stafford’, retrieve the project number,
the controlling department number and the department manager’s
last name, address and birthdate.
Relation algebra:
PNUMBER, DNUM, LNAME, ADDRESS, BDATE
(((sPLOCATION=‘STAFFORD’(PROJECT))
DNUM=DNUMBER

(DEPARTMENT))
SQL query:
Q2:
SELECT
FROM
WHERE
MGRSSN=SSN
(EMPLOYEE))
P.NUMBER,P.DNUM,E.LNAME,
E.ADDRESS, E.BDATE
PROJECT AS P,DEPARTMENT AS D,
EMPLOYEE AS E
P.DNUM=D.DNUMBER AND
D.MGRSSN=E.SSN AND
P.PLOCATION=‘STAFFORD’;
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using
Heuristics in
Query
Optimization
(4)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (5)

Heuristic Optimization of Query Trees:



The same query could correspond to many different
relational algebra expressions — and hence many different
query trees.
The task of heuristic optimization of query trees is to find a
final query tree that is efficient to execute.
Example:
Q: SELECT
FROM
WHERE
LNAME
EMPLOYEE, WORKS_ON, PROJECT
PNAME = ‘AQUARIUS’ AND
PNMUBER=PNO AND ESSN=SSN
AND BDATE > ‘1957-12-31’;
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using
Heuristics in
Query
Optimization
(6)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using
Heuristics in
Query
Optimization
(7)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in
Query
Optimization (8)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (9)
General Transformation Rules for Relational Algebra Operations:
1. Cascade of s: A conjunctive selection condition can be broken up into
a cascade (sequence) of individual s operations:
 s c1 AND c2 AND ... AND cn(R) = sc1 (sc2 (...(scn(R))...) )
2. Commutativity of s: The s operation is commutative:
 sc1 (sc2(R)) = sc2 (sc1(R))
3. Cascade of : In a cascade (sequence) of  operations, all but the last
one can be ignored:
 List1 (List2 (...(Listn(R))...) ) = List1(R)
4. Commuting s with : If the selection condition c involves only the
attributes A1, ..., An in the projection list, the two operations can be
commuted:
 A1, A2, ..., An (sc (R)) = sc (A1, A2, ..., An (R))

Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (10)
General Transformation Rules for Relational Algebra Operations
(contd.):
5. Commutativity of ( and x ): The operation is commutative as is
the x operation:
 R
C S = S
C R; R x S = S x R

6. Commuting s with (or x ): If all the attributes in the selection
condition c involve only the attributes of one of the relations being
joined—say, R—the two operations can be commuted as follows:


sc ( R
S ) = (sc (R)) S
Alternatively, if the selection condition c can be written as (c1 and c2),
where condition c1 involves only the attributes of R and condition c2
involves only the attributes of S, the operations commute as follows:

sc ( R
S ) = (sc1 (R))
(sc2 (S))
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (11)

General Transformation Rules for Relational Algebra
Operations (contd.):
7. Commuting  with (or x): Suppose that the projection list
is L = {A1, ..., An, B1, ..., Bm}, where A1, ..., An are
attributes of R and B1, ..., Bm are attributes of S. If the
join condition c involves only attributes in L, the two
operations can be commuted as follows:


L ( R
C
S ) = (A1, ..., An (R))
C
( B1, ..., Bm (S))
If the join condition C contains additional attributes not in
L, these must be added to the projection list, and a final 
operation is needed.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (12)
General Transformation Rules for Relational Algebra
Operations (contd.):
8. Commutativity of set operations: The set operations υ and
∩ are commutative but “–” is not.
9. Associativity of , x, υ, and ∩ : These four operations are
individually associative; that is, if q stands for any one of
these four operations (throughout the expression), we
have


(RqS)qT = Rq(SqT)
10. Commuting s with set operations: The s operation
commutes with υ , ∩ , and –. If q stands for any one of
these three operations, we have

sc ( R q S ) = (sc (R)) q (sc (S))
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization
(13)


General Transformation Rules for Relational Algebra
Operations (contd.):
The  operation commutes with υ.
L ( R υ S ) = (L (R)) υ (L (S))

Converting a (s, x) sequence into : If the condition c of a
s that follows a x Corresponds to a join condition, convert
the (s, x) sequence into a
as follows:
(sC (R x S)) = (R C S)

Other transformations
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization (14)

1.
2.
3.
4.
5.
6.
Outline of a Heuristic Algebraic Optimization Algorithm:
Using rule 1, break up any select operations with conjunctive
conditions into a cascade of select operations.
Using rules 2, 4, 6, and 10 concerning the commutativity of select
with other operations, move each select operation as far down the
query tree as is permitted by the attributes involved in the select
condition.
Using rule 9 concerning associativity of binary operations, rearrange
the leaf nodes of the tree so that the leaf node relations with the most
restrictive select operations are executed first in the query tree
representation.
Using Rule 12, combine a Cartesian product operation with a
subsequent select operation in the tree into a join operation.
Using rules 3, 4, 7, and 11 concerning the cascading of project and
the commuting of project with other operations, break down and
move lists of projection attributes down the tree as far as possible by
creating new project operations as needed.
Identify subtrees that represent groups of operations that can be
executed by a single algorithm.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization
(15)

Summary of Heuristics for Algebraic Optimization:
1. The main heuristic is to apply first the operations that
reduce the size of intermediate results.
2. Perform select operations as early as possible to reduce
the number of tuples and perform project operations as
early as possible to reduce the number of attributes. (This
is done by moving select and project operations as far
down the tree as possible.)
3. The select and join operations that are most restrictive
should be executed before other similar operations. (This
is done by reordering the leaf nodes of the tree among
themselves and adjusting the rest of the tree
appropriately.)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Heuristics in Query Optimization
(16)

Query Execution Plans



An execution plan for a relational algebra query consists of
a combination of the relational algebra query tree and
information about the access methods to be used for each
relation as well as the methods to be used in computing the
relational operators stored in the tree.
Materialized evaluation: the result of an operation is stored
as a temporary relation.
Pipelined evaluation: as the result of an operator is
produced, it is forwarded to the next operator in sequence.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
8. Using Selectivity and Cost Estimates in
Query Optimization (1)

Cost-based query optimization:



Estimate and compare the costs of executing a
query using different execution strategies and
choose the strategy with the lowest cost estimate.
(Compare to heuristic query optimization)
Issues


Cost function
Number of execution strategies to be considered
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (2)

Cost Components for Query Execution
1.
2.
3.
4.
5.

Access cost to secondary storage
Storage cost
Computation cost
Memory usage cost
Communication cost
Note: Different database systems may focus on
different cost components.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (3)

Catalog Information Used in Cost Functions

Information about the size of a file





number of records (tuples) (r),
record size (R),
number of blocks (b)
blocking factor (bfr)
Information about indexes and indexing attributes of a file





Number of levels (x) of each multilevel index
Number of first-level index blocks (bI1)
Number of distinct values (d) of an attribute
Selectivity (sl) of an attribute
Selection cardinality (s) of an attribute. (s = sl * r)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (4)


Examples of Cost Functions for SELECT
S1. Linear search (brute force) approach



S2. Binary search:



CS1a = b;
For an equality condition on a key, CS1a = (b/2) if the record
is found; otherwise CS1a = b.
CS2 = log2b + (s/bfr) –1
For an equality condition on a unique (key) attribute, CS2
=log2b
S3. Using a primary index (S3a) or hash key (S3b) to
retrieve a single record


CS3a = x + 1; CS3b = 1 for static or linear hashing;
CS3b = 1 for extendible hashing;
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (5)


Examples of Cost Functions for SELECT (contd.)
S4. Using an ordering index to retrieve multiple records:


S5. Using a clustering index to retrieve multiple records:


For the comparison condition on a key field with an ordering
index, CS4 = x + (b/2)
CS5 = x + ┌ (s/bfr) ┐
S6. Using a secondary (B+-tree) index:



For an equality comparison, CS6a = x + s;
For an comparison condition such as >, <, >=, or <=,
CS6a = x + (bI1/2) + (r/2)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (6)


Examples of Cost Functions for SELECT (contd.)
S7. Conjunctive selection:



S8. Conjunctive selection using a composite index:


Use either S1 or one of the methods S2 to S6 to solve.
For the latter case, use one condition to retrieve the records
and then check in the memory buffer whether each
retrieved record satisfies the remaining conditions in the
conjunction.
Same as S3a, S5 or S6a, depending on the type of index.
Examples of using the cost functions.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (7)

Examples of Cost Functions for JOIN


Join selectivity (js)
js = | (R C S) | / | R x S | = | (R
|)




C
S) | / (|R| * |S
If condition C does not exist, js = 1;
If no tuples from the relations satisfy condition C, js
= 0;
Usually, 0 <= js <= 1;
Size of the result file after join operation

| (R
C
S) | = js * |R| * |S |
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (8)


Examples of Cost Functions for JOIN (contd.)
J1. Nested-loop join:



CJ1 = bR + (bR*bS) + ((js* |R|* |S|)/bfrRS)
(Use R for outer loop)
J2. Single-loop join (using an access structure to retrieve
the matching record(s))


If an index exists for the join attribute B of S with index
levels xB, we can retrieve each record s in R and then use
the index to retrieve all the matching records t from S that
satisfy t[B] = s[A].
The cost depends on the type of index.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (9)


Examples of Cost Functions for JOIN (contd.)
J2. Single-loop join (contd.)

For a secondary index,


For a clustering index,


CJ2c = bR + (|R| * (xB + 1)) + ((js* |R|* |S|)/bfrRS);
If a hash key exists for one of the two join attributes — B of
S


CJ2b = bR + (|R| * (xB + (sB/bfrB))) + ((js* |R|* |S|)/bfrRS);
For a primary index,


CJ2a = bR + (|R| * (xB + sB)) + ((js* |R|* |S|)/bfrRS);
CJ2d = bR + (|R| * h) + ((js* |R|* |S|)/bfrRS);
J3. Sort-merge join:


CJ3a = CS + bR + bS + ((js* |R|* |S|)/bfrRS);
(CS: Cost for sorting files)
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Using Selectivity and Cost Estimates in
Query Optimization (10)

Multiple Relation Queries and Join Ordering



A query joining n relations will have n-1 join operations, and
hence can have a large number of different join orders
when we apply the algebraic transformation rules.
Current query optimizers typically limit the structure of a
(join) query tree to that of left-deep (or right-deep) trees.
Left-deep tree:

A binary tree where the right child of each non-leaf node is
always a base relation.


Amenable to pipelining
Could utilize any access paths on the base relation (the right
child) when executing the join.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
9. Overview of Query Optimization in
Oracle

Oracle DBMS V8

Rule-based query optimization: the optimizer chooses
execution plans based on heuristically ranked operations.


Cost-based query optimization: the optimizer examines
alternative access paths and operator algorithms and
chooses the execution plan with lowest estimate cost.



(Currently it is being phased out)
The query cost is calculated based on the estimated usage of
resources such as I/O, CPU and memory needed.
Application developers could specify hints to the ORACLE
query optimizer.
The idea is that an application developer might know more
information about the data.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
10. Semantic Query Optimization



Semantic Query Optimization:
 Uses constraints specified on the database schema in order to
modify one query into another query that is more efficient to
execute.
Consider the following SQL query,
SELECT
E.LNAME, M.LNAME
FROM
EMPLOYEE E M
WHERE
E.SUPERSSN=M.SSN AND E.SALARY>M.SALARY
Explanation:
 Suppose that we had a constraint on the database schema that
stated that no employee can earn more than his or her direct
supervisor. If the semantic query optimizer checks for the
existence of this constraint, it need not execute the query at all
because it knows that the result of the query will be empty.
Techniques known as theorem proving can be used for this
purpose.
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Summary
0. Introduction to Query Processing
1. Translating SQL Queries into Relational Algebra
2. Algorithms for External Sorting
3. Algorithms for SELECT and JOIN Operations
4. Algorithms for PROJECT and SET Operations
5. Implementing Aggregate Operations and Outer Joins
6. Combining Operations using Pipelining
7. Using Heuristics in Query Optimization
8. Using Selectivity and Cost Estimates in Query
Optimization
9. Overview of Query Optimization in Oracle
10. Semantic Query Optimization
Copyright © 2011 Ramez Elmasri and Shamkant Navathe